asaad abdullah

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Simulation and implementation a fuzzy logic control for a refrigeration drive based on the variable speed cycle

AIP Conference Proceedings

In recent years, there has been a need to design highly efficient cooling devices that are used i... more In recent years, there has been a need to design highly efficient cooling devices that are used in the manufacture of nanomaterials and some medical treatments. The objective of this paper is to design the membership shape of a variable speed cycle variable speed fuzzy logic controller for a cooling engine. The fuzzy logic controller sets the sampling time for the SVPWM in the inverter system in the variable speed drive cooling cycle. The research focuses on the systematic design of membership ranges for key design factors such as control error and control error rate. The upper and lower limits of the membership ranges are prepared from the static characteristics data obtained by modelling MATLAB. Membership range was tested on control error and error rate control experiments. The effect of sampling time on control performance has been investigated and has been shown to have a significant effect on the refrigeration cycle control performance of a variable speed drive, the results of proposed device demonstrate settling time less than (15 sec).

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Design of Optimal Fuzzy Controller for Heat Exchanger Temperature

Journal of Engineering Science and Technology Review

The heat exchanger system (HES) is widely used in petroleum industries as well as in chemical app... more The heat exchanger system (HES) is widely used in petroleum industries as well as in chemical applications because it may maintain the process of controlling the pressure or temperature of a liquid. In addition, HES is often complicated, and expensive devices. The heat exchanger types used in simpler applications are inexpensive but nonlinear, and their dynamic response is very weak; Therefore, it is quite difficult to be modelled, and their dynamics operation is hard to control. In this work, several methods are applied to the heat exchanger model and its control to select the appropriate model and control unit in complex working conditions. The problems that affect the dynamic parameter setting adopted by the traditional incremental proportional integrated derivative (PID) and the intelligently designed control systems which combine a fuzzy controller that adapts to a PID controller that has a better performance are studied. The traditional PID controller is always built using MATLAB-Simulink, and two fuzzy logic controllers are designed. One of the designs is based on the sine-cosine algorithm (SCA), and another uses particle swarm optimization (PSO). Results show that the sine-cosine algorithm (FLC_SCA) controller has excellent response and strength compared to PID and particle swarm dependent optimisation (FLC_PSO) is suitable for complex time-delay systems.

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A temperature measuring of combustion machinery systems using light absorbing of gas

3RD INTERNATIONAL SCIENTIFIC CONFERENCE OF ALKAFEEL UNIVERSITY (ISCKU 2021), 2022

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Simulation of Crude Oil Transportation with Drag Reduction Agents Using k-ϵ and k-ω Models

European Journal of Computational Mechanics, 2021

This work explores the possibility of using Newtonian turbulence k−ϵ and k−ω models for modelling... more This work explores the possibility of using Newtonian turbulence k−ϵ and k−ω models for modelling crude oil flow in pipelines with drag reduction agents. These models have been applied to predict the friction factor, pressure drop and the drag reduction percentage. The simulation results of both models were compared with six published experimental data for crude oil flow in pipes with different types of drag reduction agents. The velocity near the wall was determined using the log law line of Newtonian fluid equation and by changing the parameter ΔB to achieve an excellent agreement with experimental data. Simulated data for k−ϵ model shows better agreement with most experimental data than the k−ω turbulence model.

Effect of Temperature on The Performance of Naphtha and Kerosene as Viscosity Reduction Agents for Improving Flow Ability of Basrah-Iraq Heavy Crude Oil

Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 2021

Naphtha and kerosene are mixed with Iraqi heavy crude oil at different concentrations rounded bet... more Naphtha and kerosene are mixed with Iraqi heavy crude oil at different concentrations rounded between (3-12) wt.%, in order to reduce viscosity to enhance its followability. This research investigated drag reduction (%Dr) in heavy oil mixtures at different flow rates (2 to 10 m3/hr) in temperature range 20-50C. The experimental results proved that Naphtha offered 40% reduction in pressure drop. The Power law model was adopted in this study to empirically correlate fiction factor (f) and the percentage of drag reduction(%Dr) from experimental data for Reynolds number range (534– 14695) and the concentration range from 3 to12 wt.%.

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Estimation and Optimization Cutting Conditions of Surface Roughness in Hard Turning Using Taguchi Approach and Artificial Neural Network

Advanced Materials Research, 2012

One of the most important requirements of part manufacturing is the surface quality. This is so b... more One of the most important requirements of part manufacturing is the surface quality. This is so because the most important part is meeting the specific requirements of customers. The surface roughness is a leading indicator of the quality of the machined surface Parts. In the present work in an experimental study to achieve by application of Taguchi method to investigate the effect of three parameters, which known as cutting speeds of (45, 90, and 135 m/min), feed rate of (0.1, 0.2, and 0.3 mm/rev), and cut depth of (0.05, 0.1, and 0.15 mm) on performance measure of surface roughness (Ra). Thus to determine the optimal levels and to analyze the cutting parameter’s effect on the surface finish values by employing different method of Orthogonal array, S/N ratio, analysis of variance (ANOVA). During our work two models for prediction have been used. The first one is known as the method of regression analysis, and the second is the method of Adaptive - Neural Network (ANN) relying on pr...

Cutting Tool Geometry and Parameters Effects on the Force and Temperature in Turning Steel 1040 Using Finite Element Analysis

Advanced Materials Research, 2012

In recent years, applications have been proven finite-element method (FEM) in metal-cutting opera... more In recent years, applications have been proven finite-element method (FEM) in metal-cutting operations to be effective process in the study of cutting and chip formation. In this study, the simulation results are useful for both researchers and machine tool manufacturers for improving the design of cutting parameters. Finite-element analysis (FEA) that used in this study of simulation the cutting parameters and tool geometries effects on the force and temperature in turning AISI 1040. The simulation parameters that used in this study are cutting speed (75 - 300 m/min),feed rate (0.2 mm/rev), cut depth (0.75-1.5 mm), and rake angle (0-20 °). The results of cutting forces were (240 – 520 N), the temperature were (300-420 °C), and the heat rate (14202.3-83772.8 W/mm3) on the cutting edge. The simulation process also show that the increase of cutting speed leads to decrease in the cutting forces, while it has increasing in temperature, and heat rate. Also, the results show that the incr...

EMG-muscle force estimation model based on back-propagation neural network

2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings, 2012

Electromyography (EMG) signal precede many information about muscle activity, which can be used t... more Electromyography (EMG) signal precede many information about muscle activity, which can be used to analyze muscle functions, such as muscle forces. Muscle forces estimation is important for orthopedists, bio-mechanists, and physical therapists because of joint contact forces. In this work, EMG signal is used to estimate human arm muscle force by implementing a new Back-Propagation neural network (BPNN) model. The proposed model, uses the rectified smoothed electromyography (RSEMG) signals as input to generate the estimated muscle force as an output. To train the system, the Biceps-brachii muscle force was used. To validate our model, the Brachioradialis, Triceps-brachii, and Flexor carpi ulnaris muscles' signals were used. To measure the performance of the model, the BPNN output estimated force is compared with the Hill-Type force estimation. Our results illustrate that. The output of our model is accurate compared with Hell-type model. This can be proved through the regression value of our model which exceeded 99%. We have used four muscles of the arm to show that our model can estimate the force of any muscle of the hand, which makes it a global hand muscle force estimator.

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Estimating human arm's muscle force using Artificial Neural Network

2012 IEEE International Symposium on Medical Measurements and Applications Proceedings, 2012

Muscle force models have many applications in human-machine motion analysis, human-machine interf... more Muscle force models have many applications in human-machine motion analysis, human-machine interfacing, rehabilitation and robotics. A Hill-type model was used to estimate muscle force. In this work, we are going to introduce a new model to estimate human muscle force. Our model, estimates human muscle force based on a rectified smoothed electromyography (RSEMG) signal using the back-propagation Artificial Neural Network

Prediction and Optimization Approaches for Modeling and Selection of Optimum Machining Parameters in CNC down Milling Operation

Research Journal of Applied Sciences, Engineering and Technology, 2014

In this study, we suggested intelligent approach to predict and optimize the cutting parameters w... more In this study, we suggested intelligent approach to predict and optimize the cutting parameters when down milling of 45# steel material with cutting tool PTHK-(Ø10*20C*10D*75L) -4F-1.0R under dry condition. The experiments were performed statistically according to four factors with three levels in Taguchi experimental design method. Adaptive Neuro-fuzzy inference system is utilized to establish the relationship between the inputs and output parameter exploiting the Taguchi orthogonal array L27. The Particle Swarm Optimized-Adaptive Neuro-Fuzzy Inference System (PSOANFIS) is suggested to select the best cutting parameters providing the lower surface through from the experimental data using ANFIS models to predict objective functions. The PSOANFIS optimization approach that improves the surface quality from 0.212 to 0.202, as well as the cutting time is also reduced from 7.5 to 4.78 sec according to machining parameters before and after optimization process. From these results, it can be readily achieved that the advanced study is trusted and suitable for solving other problems encountered in metal cutting operations and the same surface roughness.

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